We present the experimental demonstration of nondestructive detection of ^(171)Yb atoms in a magneto-optical trap(MOT) based on phase shift measurement induced by the atoms on a weak off-resonant laser beam. After loa...We present the experimental demonstration of nondestructive detection of ^(171)Yb atoms in a magneto-optical trap(MOT) based on phase shift measurement induced by the atoms on a weak off-resonant laser beam. After loading a green MOT of ^(171)Yb atoms, the phase shift is obtained with a two-color Mach–Zehnder interferometer by means of ±45 MHz detuning with respect to the ^(1)S_(0)–^(1)P_(1) transition. We measured a phase shift of about 100 mrad corresponding to an atom count of around 5 × 10^(5). This demonstrates that it is possible to obtain the number of atoms without direct destructive measurement compared with the absorption imaging method. This scheme could be an important approach towards a high-precision lattice clock for clock operation through suppression of the impact of the Dick effect.展开更多
Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and...Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.展开更多
Existing nondestructive detection methods were adopted to test the compressive strength of grouted concrete block masonry,i.e.the rebound method,pulling-out method and core drilling method were employed to test the st...Existing nondestructive detection methods were adopted to test the compressive strength of grouted concrete block masonry,i.e.the rebound method,pulling-out method and core drilling method were employed to test the strength of block,mortar and grouted concrete,respectively.The suitability of these methods for the testing of strength of grouted concrete block masonry was discussed,and the comprehensive strength of block masonry was appraised by combining existing nondestructive or micro-destructive detection methods.The nondestructive detection test on 25 grouted concrete block masonry specimens was carried out.Experimental results show that these methods mentioned above are applicable for the strength detection of grouted concrete block masonry.Moreover,the formulas of compressive strength,detection methods and proposals are given as well.展开更多
The ultrasonic computed tomography （USCT） method is derived from the basic principles of X-ray section scanning. This method records the arriving times of ultrasonic wave between the probes and the sources to ealcul...The ultrasonic computed tomography （USCT） method is derived from the basic principles of X-ray section scanning. This method records the arriving times of ultrasonic wave between the probes and the sources to ealculate the elastic wave velocity values in the section using the arrival times. Through analyzed the distribution Of elastic wave velocity in aim area, the information of the strength and the homogeneity of the investigated zone could be got indirectly. The authors introduced the operational principle of USCT and a practical case of using this method to detect the interior defects in large scale concrete structural member. Compared with other exploration methods, this method is more efficient and accurate.展开更多
Accurately identifying the location and type of internal defects in gas-insulated switchgear(GIS)is a challenge.To address this challenge,this study proposes a novel method for the nondestructive detection of GIS inte...Accurately identifying the location and type of internal defects in gas-insulated switchgear(GIS)is a challenge.To address this challenge,this study proposes a novel method for the nondestructive detection of GIS internal defects.This method is based on x-ray digital radiography(X-DR)technology and an improved real-time models for object detection(RTMdet)algorithm,namely GIS-specific localised internal defect-RTMdet.Firstly,the X-DR images of GIS are preprocessed by dynamic limit adaptive histogram equalisation algorithm to improve the images contrast.Then,a convolution shuffle upsample module for upsampling is proposed,which enlarges the defect feature map by multi-convolution and pixel shuffling,reduces the information loss,and enhances the interaction between the feature information.Finally,both the multi-channel attention net and the global attention mechanism are integrated into the neck network for enhancing local feature extraction and global information association.Experiments demonstrate that the pro-posed method achieves a mean average precision@0.5:0.95 of 94.9%,showcasing excellent overall performance and generalisation ability,and is more suitable for accurate nondestructive detection of internal defects of GIS in complex scenarios.展开更多
In order to quickly distinguish infertile eggs from fertile eggs,the hyperspectral imaging technology consisting of imaging and spectral information was used for detecting the fertile information of eggs.Before hatchi...In order to quickly distinguish infertile eggs from fertile eggs,the hyperspectral imaging technology consisting of imaging and spectral information was used for detecting the fertile information of eggs.Before hatching eggs were incubated,a hyperspectral imaging system(wavelength between 400 to 1000 nm)was used to acquire the images one-by-one manually.The characteristic information of ratios of length to short axis,elongation,roundness and the ratios of the yolk area to the whole area was extracted based on the images.The normalization method was used as the spectral data preprocessing,and then 155 spectral characteristic variables were extracted from 520 nm waveband through the correlation coefficient method.Principal component analysis(PCA)method was adopted to reduce the dimensions of image-spectrum fusion information;the top six principal components were extracted.Support vector machine(SVM)method was used to establish classification of fertile and infertile eggs models,which are based on image,spectrum and image-spectrum fusion information respectively.The accuracy rates of the SVM models were 84.00%,90.00%and 93.00%respectively.The experimental results show that the model based on image-spectrum fusion information technology is superior to the single information model.Hyperspectral transmission imaging technology is effective and feasible to detect the fertile hatching eggs before incubation.展开更多
Electromagnetic self-induction theory and computer are adopted and study of online monitoring technique for wire-core belt is conducted, the study shows that there is direct proportion between distance Ⅰ of broken en...Electromagnetic self-induction theory and computer are adopted and study of online monitoring technique for wire-core belt is conducted, the study shows that there is direct proportion between distance Ⅰ of broken ends and output volt Ⅴ, when Ⅰ ≥60 mm, Ⅴ keeps constantly, the running speed v of wire-core belt has no big effect on output volt Ⅴ, there is inverse proportion between the height h from probe to the surface of the belt and output volt Ⅴ, when h≥30 mm, Ⅴ tends to be zero. Based on the test result, on-line monitoring installation is developed, the practice proved that the accuracy of broken wire monitoring can be above 95%, the monitoring accuracy of joint twitch can be 0 .04 Ⅴ/mm.展开更多
[Objective] The research aimed at investigating the application of laser speckle technology in agricultural products detection.[Method] This article described the basic principle of the laser speckle technology,provid...[Objective] The research aimed at investigating the application of laser speckle technology in agricultural products detection.[Method] This article described the basic principle of the laser speckle technology,provided a review of application development of the laser speckle technology in agricultural products detection,analyzed the problems in agriculture products detection using laser speckle technology and described the prospects of laser speckle technology in agricultural products detection.[Result] The laser speckle technology is a non-destructive detection technology for quality determination of agricultural products,which can be used to classify the agricultural products reasonably according to the quality of agricultural products.[Conclusion] The article provided reference and consult for laser speckle detection technology research.展开更多
Tungsten(W)is the leading plasma-facing candidate material for the International Thermonuclear Experimental Reactor and next-generation fusion reactors.The impact of synergistic helium(He),irradiation-induced microstr...Tungsten(W)is the leading plasma-facing candidate material for the International Thermonuclear Experimental Reactor and next-generation fusion reactors.The impact of synergistic helium(He),irradiation-induced microstructural changes,and the corresponding thermal-mechanical property degradation of W are critically important but are not well understood yet.Predicting the performance of W in fusion environments requires understanding the fundamentals of He-defect interactions and the resultant He bubble nucleation and growth in W.In this study,He retention in helium-ion-implanted W was assessed using neutron depth profiling(NDP),laser ablation mass spectrometry(LAMS),and thermal desorption spectroscopy(TDS)following 10 keV room-temperature He implantation at various fluences.These three experimental techniques enabled the determination of the He depth profile and retention in He-implanted W.A cluster dynamics model based on the diffusion-reaction rate theory was applied to interpret the experimental data.The model successfully predicted the He spatial depth-dependent profile in He-implanted W,which was in good agreement with the LAMS measurements.The model also successfully captured the major features of the He desorption spectra observed in the THDS measurements.The NDP quantified total He concentration values for the samples;they were similar to those estimated by LAMS.However,the depth profiles from NDP and LAMS were not comparable due to several factors.The combination of modeling and experimentation enabled the identification of possible trapping sites for He in W and the evolution of He-defect clusters during the TDS thermal annealing process.展开更多
Nondestructive evaluation of melon quality is in great need of comprehensive study.Soluble solids content(SSC)and firmness are the two indicators of melon internal quality that mostly a®ect consumer acceptance.To...Nondestructive evaluation of melon quality is in great need of comprehensive study.Soluble solids content(SSC)and firmness are the two indicators of melon internal quality that mostly a®ect consumer acceptance.To provide guidance for fruit classification,internal quality standards was preliminarily established through sensory test,as:Melon with SSC over 12Brix,firmness 4–5.5 kgf·cm^(-2)2 were considered as satisfactory class sample;and SSC over 10Brix,¯rmness 3.5–6.5 kgf·cm^(-2) as average class sample.The near infrared(NIR)nondestructive detection program was set as spectra collected from the stylar-end,Brix expressed by the average SSC of inner and outer mesocarp,each cultivar of melon was detected with its own optimum integration time,and the second derivative algorithm was used to equalize them.Using wavelength selected by genetic algorithms(GA),a robust SSC model of mix-cultivar melon was established,the root mean standard error of cross-validation(RMSECV)was 0.99 and the ratio performance deviation(RPD)nearly reached 3.0,which almost could meet the accuracy requirement of 1.5Brix.Firmness model of mix-cultivar melon was acceptable but inferior.展开更多
The relationship between ultrasonic nonlinearity and microstructure of the liner was studied during the whole curing process by ultrasonic transmission method and infrared spectroscopy.Nonlinearity of input instrument...The relationship between ultrasonic nonlinearity and microstructure of the liner was studied during the whole curing process by ultrasonic transmission method and infrared spectroscopy.Nonlinearity of input instrumentation was minimized by the natural filtering effect of piezoelectric discs and the maximum excitation energy was acquired simultaneously so as to improve the accuracy of the measuring data.The experimental results indicate that in the liner curing reaction at40℃ultrasonic nonlinearity parameter decreases gradually after a sharp decline,which is consistent with the outcome of infrared spectroscopy as the curing degree increases.The research suggests an effective nondestructive approach to detect the curing properties of the liner in a nonlinear ultrasonic way.展开更多
Subsurface defects were fluorescently tagged with nanoscale quantum dots and scanned layer by layer using confocal fluorescence microscopy to obtain images at various depths. Subsurface damage depths of fused silica o...Subsurface defects were fluorescently tagged with nanoscale quantum dots and scanned layer by layer using confocal fluorescence microscopy to obtain images at various depths. Subsurface damage depths of fused silica optics were characterized quantitatively by changes in the fluorescence intensity of feature points. The fluorescence intensity vs scan depth revealed that the maximum fluorescence intensity decreases sharply when the scan depth exceeds a critical value. The subsurface damage depth could be determined by the actual embedded depth of the quantum dots. Taper polishing and magnetorheological finishing were performed under the same conditions to verify the effectiveness of the nondestructive fluorescence method. The results indicated that the quantum dots effectively tagged subsurface defects of fused-silica optics, and that the nondestructive detection method could effectively evaluate subsurface damage depths.展开更多
Watermelon is a popular fruit in the world with soluble solids content （SSC） being one of the major characteristics used for assessing its quality. This study was aimed at obtaining a method for nondestructive SSC d...Watermelon is a popular fruit in the world with soluble solids content （SSC） being one of the major characteristics used for assessing its quality. This study was aimed at obtaining a method for nondestructive SSC detection of watermelons by means of visible/near infrared （Vis/NIR） diffuse transmittance technique. Vis/NIR transmittance spectra of intact watermelons were acquired using a low-cost commercially available spectrometer operating over the range 350-1000 nm. Spectra data were analyzed by two multivariate calibration techniques： partial least squares （PLS） and principal component regression （PCR） methods. Two experiments were designed for two varieties of watermelons [Qilin （QL）, Zaochunhongyu （ZC）], which have different skin thickness range and shape dimensions. The influences of different data preprocessing and spectra treatments were also investigated. Performance of different models was assessed in terms of root mean square errors of calibration （RMSEC）, root mean square errors of prediction （RMSEP） and correlation coefficient （r） between the predicted and measured parameter values. Results showed that spectra data preprocessing influenced the performance of the calibration models. The first derivative spectra showed the best results with high correlation coefficient of determination [r=0.918 （QL）; r=0.954 （ZC）], low RMSEP [0.65 °Brix （QL）; 0.58 °Brix （ZC）], low RMSEC [0.48 °Brix （QL）; 0.34°Brix （ZC）] and small difference between the＇RMSEP and the RMSEC by PLS method. The nondestructive Vis/NIR measurements provided good estimates of SSC index of watermelon, and the predicted values were highly correlated with destructively measured values for SSC. The models based on smoothing spectra （Savitzky-Golay filter smoothing method） did not enhance the performance of calibration models obviously. The results indicated the feasibility of Vis/NIR diffuse transmittance spectral analysis for predicting watermelon SSC in a nondestructive way.展开更多
t The aim of this study was to investigate the feasibility of detecting potassium sorbate(PS)and sorbic acid(SA)in agricultural products using THz time-domain spectroscopy(THz-TDS).The absorption spectra of PS and SA ...t The aim of this study was to investigate the feasibility of detecting potassium sorbate(PS)and sorbic acid(SA)in agricultural products using THz time-domain spectroscopy(THz-TDS).The absorption spectra of PS and SA were measured from 0.2 to 1.6 THz at room temperature.The main characteristic absorption peaks of PS and SA in polyethylene and powdered agricultural products with different weight ratios were detected and analyzed.Interval partial least squares(iPLS)combined with a particle swarm optimization and support vector classification(PSO-SVC)algorithm was proposed in this paper.iPLS was used for frequency optimization,and the PSO-SVC algorithm was used for spectrum analysis of the preservative content based on the optimal spectrum ranges.Optimized PSO-SVC models were obtained when the THz spectrum from the PS/SA mixture was divided into 11 or 12 subintervals.The optimal penalty parameter c and kernel parameter g were found to be 1.284 and 0.863 for PS(0.551-1.487 THz),1.374 and 0.906 for SA(0.454-1.216 THz),respectively.The preliminary results indicate that THz-TDS can be an effective nondestructive analytical tool used for the quantitative detection of additives in agricultural products.展开更多
This paper presents a new algorithm to predict locations and severities of damage in structures by changing modal parameters. An existing algorithm of damage detection is reviewed and the new algorithm is formulated t...This paper presents a new algorithm to predict locations and severities of damage in structures by changing modal parameters. An existing algorithm of damage detection is reviewed and the new algorithm is formulated to improve the accuracy of damage locating and severity estimation by eliminating the erratic assumptions and limits in the existing algorithm. The damage prediction accuracy is numerically assessed for each algorithm when applied to a two-dimensional frame structure for which pre-damage and post-damage modal parameters are available for only a few modes of vibration. The analysis results illustrate the improved accuracy of the new algorithm when compared to the existing algorithm.展开更多
According to the structure and stress trait of bearing bolts,a lateral-vibrationmechanics model was established for them,and the relation between lateral-vibration frequencyand axial load was analyzed;then,lateral-vib...According to the structure and stress trait of bearing bolts,a lateral-vibrationmechanics model was established for them,and the relation between lateral-vibration frequencyand axial load was analyzed;then,lateral-vibration trait of bearing bolts was studiedthrough laboratory simulation test.The results indicate that vibration frequency of boltsupport system increases as well as axial force,the detection on axial load of bolts can bemade by generating lateral vibration of bearing bolts.Theoretical and experimental researchresults show that frequency method is effective for detecting the axial force of boltsupport system.展开更多
Recent approaches to the internal quality inspection of apples with the application of hyperspectral imaging technology are highly cost-intensive because of labor involvement for the data collection on a fixed posture...Recent approaches to the internal quality inspection of apples with the application of hyperspectral imaging technology are highly cost-intensive because of labor involvement for the data collection on a fixed posture and manual selection of the region of interest(RoI).In addition,several studies have repeated the data acquisition for the same apple.Current methods cannot meet the automation requirements of the sorting line.Therefore,this study proposed a novel method for automatically selecting RoI in hyperspectral images of apples with random poses.Firstly,the preliminary RoI selection of apple hyperspectral image was carried out,followed by the performance of histogram statistics of each pixel with spectral intensity at 700 nm wavelength.The top 40%area of the spectral intensity was reserved to obtain the magnitude relationship of the spectral intensity of each pixel point and a morphological erosion operation.Original apple RoI was acquired and overexposed pixels were removed with spectral intensity greater than 3900(maximum 4095)in the reserved area at 700 nm.Secondly,the relationship between apple size and prediction accuracy was measured for the in-depth RoI analysis.A partial least square regression(PLSR)model was established between the average spectrum and apple sugar content of RoI with different sizes.Finally,the established model with the top 70%of the spectral intensity achieved the best prediction accuracy.Non-destructive estimation of apple sugar content was performed through hyperspectral imaging technology with reference to the proposed RoI selection method.A competitive adaptive reweighted sampling algorithm along the PLSR(CARS-PLSR)model was established after black-and-white correction and standard normal transformation(SNV)preprocessing and obtained the highest prediction accuracy.The determination coefficient of cross-validation(R_(cv))and root mean square error of cross-validation(RMSECV)were 0.9595 and 0.3203°Brix,respectively.The determination coefficient of prediction(R_(p))was 0.9308,and the root mean square error of prediction(RMSEP)was 0.4681°Brix.Results proved that the auto-selection of RoI is an efficient and accurate method,which can provide a foundation in practical application for online apple grading systems with hyperspectral imaging technology.展开更多
In this paper,we propose a photonic terahertz(THz)continuous-wave computed tomography(CT)system employing an optical frequency comb and specialized imaging algorithms.Our work leverages the system to offer unique adva...In this paper,we propose a photonic terahertz(THz)continuous-wave computed tomography(CT)system employing an optical frequency comb and specialized imaging algorithms.Our work leverages the system to offer unique advantages in detecting and analyzing samples that are challenging for traditional 2D scanning systems.Our experimental results,operating at 330 GHz,reach an exceptionally low amplitude standard deviation of 0.016 m V.Additionally,the proposed system performs nondestructive CT detection with a 0.5 mm error margin and obtains enhanced image quality,showing its great promise for implementing THz-CT imaging with high robustness and resolution.展开更多
A new method to predict the seed vigor of rice was developed to control adulteration during the seed trading process and to address the deficiencies of traditional manual detection methods.Low-field nuclear magnetic r...A new method to predict the seed vigor of rice was developed to control adulteration during the seed trading process and to address the deficiencies of traditional manual detection methods.Low-field nuclear magnetic resonance(LF-NMR)technique was used to detect the vigor of rice seeds.Four varieties(Beijing-1,Qianchonglang-2,Yanfeng-47 and Shennong-265)of rice seeds from the Rice Research Institute of Shenyang Agricultural University were chosen for the experiment.The transverse relaxation time T_(2),T_(21) and T_(22) were observed in the experiment.The peak start time of free water(transverse relaxation time T_(22)),signal amplitude of bound water(transverse relaxation time T_(21)),and moisture content decreased with the decrease in the vigor of the seeds.There were no obvious trends observed for the top of the peak and the end point of the transverse relaxation time T_(22).In addition,the start,top,and end time of the peak(transverse relaxation time T_(21)),and the signal amplitude of bound water showed no consistent changes.The results indicated that LF-NMR could be used as a method to distinguish the vigor of rice seeds rapidly.This study provided theoretical basis and technical support for the rapid detection of rice seed vigor.展开更多
Nowadays,China stands as the global leader in terms of potato planting area and total potato production.The rapid and nondestructive detection of the potato quality before processing is of great significance in promot...Nowadays,China stands as the global leader in terms of potato planting area and total potato production.The rapid and nondestructive detection of the potato quality before processing is of great significance in promoting rural revitalization and augmenting farmers’income.However,existing potato quality sorting methods are primarily confined to theoretical research,and the market lacks an integrated intelligent detection system.Therefore,there is an urgent need for a post-harvest potato detection method adapted to the actual production needs.The study proposes a potato quality sorting method based on cross-modal technology.First,an industrial camera obtains image information for external quality detection.A model using the YOLOv5s algorithm to detect external green-skinned,germinated,rot and mechanical damage defects.VIS/NIR spectroscopy is used to obtain spectral information for internal quality detection.A convolutional neural network(CNN)algorithm is used to detect internal blackheart disease defects.The mean average precision(mAP)of the external detection model is 0.892 when intersection of union(IoU)=0.5.The accuracy of the internal detection model is 98.2%.The real-time dynamic defect detection rate for the final online detection system is 91.3%,and the average detection time is 350 ms per potato.In contrast to samples collected in an ideal laboratory setting for analysis,the dynamic detection results of this study are more applicable based on a real-time online working environment.It also provides a valuable reference for the subsequent online quality testing of similar agricultural products.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos. U20A2075,11803072,and 12374467)the Innovation Program for Quantum Science and Technology (Grant No. 2021ZD0300902)the Hubei Provincial Science and Technology Major Project (Grant No. ZDZX2022000004)。
文摘We present the experimental demonstration of nondestructive detection of ^(171)Yb atoms in a magneto-optical trap(MOT) based on phase shift measurement induced by the atoms on a weak off-resonant laser beam. After loading a green MOT of ^(171)Yb atoms, the phase shift is obtained with a two-color Mach–Zehnder interferometer by means of ±45 MHz detuning with respect to the ^(1)S_(0)–^(1)P_(1) transition. We measured a phase shift of about 100 mrad corresponding to an atom count of around 5 × 10^(5). This demonstrates that it is possible to obtain the number of atoms without direct destructive measurement compared with the absorption imaging method. This scheme could be an important approach towards a high-precision lattice clock for clock operation through suppression of the impact of the Dick effect.
基金National Natural Science Foundation of China(Grant number:11904327,61905223,and 62073299)Training Plan of Young Backbone Teachers in Universities of Henan Province(2023GGJS087)+1 种基金Henan Provincial Science and Technology Research Project(222102110279,222102210085,and 242102210157)Project of Central Plains Science and Technology Innovation Leading Talents(224200510026).
文摘Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.
文摘Existing nondestructive detection methods were adopted to test the compressive strength of grouted concrete block masonry,i.e.the rebound method,pulling-out method and core drilling method were employed to test the strength of block,mortar and grouted concrete,respectively.The suitability of these methods for the testing of strength of grouted concrete block masonry was discussed,and the comprehensive strength of block masonry was appraised by combining existing nondestructive or micro-destructive detection methods.The nondestructive detection test on 25 grouted concrete block masonry specimens was carried out.Experimental results show that these methods mentioned above are applicable for the strength detection of grouted concrete block masonry.Moreover,the formulas of compressive strength,detection methods and proposals are given as well.
基金Supported by Project of the National High Technology Research and Development Program of China(No.2007AA06Z215)
文摘The ultrasonic computed tomography （USCT） method is derived from the basic principles of X-ray section scanning. This method records the arriving times of ultrasonic wave between the probes and the sources to ealculate the elastic wave velocity values in the section using the arrival times. Through analyzed the distribution Of elastic wave velocity in aim area, the information of the strength and the homogeneity of the investigated zone could be got indirectly. The authors introduced the operational principle of USCT and a practical case of using this method to detect the interior defects in large scale concrete structural member. Compared with other exploration methods, this method is more efficient and accurate.
基金National Engineering Research Center of UHV Technology and New Electrical Equipment Basis of China Southern Power Grid Research Institute Co.,Ltd,Grant/Award Number:NERCUTNEEB-2022-KF-08。
文摘Accurately identifying the location and type of internal defects in gas-insulated switchgear(GIS)is a challenge.To address this challenge,this study proposes a novel method for the nondestructive detection of GIS internal defects.This method is based on x-ray digital radiography(X-DR)technology and an improved real-time models for object detection(RTMdet)algorithm,namely GIS-specific localised internal defect-RTMdet.Firstly,the X-DR images of GIS are preprocessed by dynamic limit adaptive histogram equalisation algorithm to improve the images contrast.Then,a convolution shuffle upsample module for upsampling is proposed,which enlarges the defect feature map by multi-convolution and pixel shuffling,reduces the information loss,and enhances the interaction between the feature information.Finally,both the multi-channel attention net and the global attention mechanism are integrated into the neck network for enhancing local feature extraction and global information association.Experiments demonstrate that the pro-posed method achieves a mean average precision@0.5:0.95 of 94.9%,showcasing excellent overall performance and generalisation ability,and is more suitable for accurate nondestructive detection of internal defects of GIS in complex scenarios.
基金This work was supported by Special Fund for Agro-scientific Research in the Public Interest(Grant No.201303084)。
文摘In order to quickly distinguish infertile eggs from fertile eggs,the hyperspectral imaging technology consisting of imaging and spectral information was used for detecting the fertile information of eggs.Before hatching eggs were incubated,a hyperspectral imaging system(wavelength between 400 to 1000 nm)was used to acquire the images one-by-one manually.The characteristic information of ratios of length to short axis,elongation,roundness and the ratios of the yolk area to the whole area was extracted based on the images.The normalization method was used as the spectral data preprocessing,and then 155 spectral characteristic variables were extracted from 520 nm waveband through the correlation coefficient method.Principal component analysis(PCA)method was adopted to reduce the dimensions of image-spectrum fusion information;the top six principal components were extracted.Support vector machine(SVM)method was used to establish classification of fertile and infertile eggs models,which are based on image,spectrum and image-spectrum fusion information respectively.The accuracy rates of the SVM models were 84.00%,90.00%and 93.00%respectively.The experimental results show that the model based on image-spectrum fusion information technology is superior to the single information model.Hyperspectral transmission imaging technology is effective and feasible to detect the fertile hatching eggs before incubation.
文摘Electromagnetic self-induction theory and computer are adopted and study of online monitoring technique for wire-core belt is conducted, the study shows that there is direct proportion between distance Ⅰ of broken ends and output volt Ⅴ, when Ⅰ ≥60 mm, Ⅴ keeps constantly, the running speed v of wire-core belt has no big effect on output volt Ⅴ, there is inverse proportion between the height h from probe to the surface of the belt and output volt Ⅴ, when h≥30 mm, Ⅴ tends to be zero. Based on the test result, on-line monitoring installation is developed, the practice proved that the accuracy of broken wire monitoring can be above 95%, the monitoring accuracy of joint twitch can be 0 .04 Ⅴ/mm.
基金Supported by Project of Beijing Natural Science Foundation(6113022)~~
文摘[Objective] The research aimed at investigating the application of laser speckle technology in agricultural products detection.[Method] This article described the basic principle of the laser speckle technology,provided a review of application development of the laser speckle technology in agricultural products detection,analyzed the problems in agriculture products detection using laser speckle technology and described the prospects of laser speckle technology in agricultural products detection.[Result] The laser speckle technology is a non-destructive detection technology for quality determination of agricultural products,which can be used to classify the agricultural products reasonably according to the quality of agricultural products.[Conclusion] The article provided reference and consult for laser speckle detection technology research.
基金supported by the U.S.Department of EnergyOffice of Science+5 种基金Fusion Energy Sciences Programunder Contract No.DE-AC05-00OR22725 with UT-BattelleLLCfinancial support from the US Department of EnergyOffice of Fusion Energy Science under grant DOE-DE-SC000661 at The University of Tennessee-KnoxvilleJLW and HCM were funded by the National Institute of Standards and Technology。
文摘Tungsten(W)is the leading plasma-facing candidate material for the International Thermonuclear Experimental Reactor and next-generation fusion reactors.The impact of synergistic helium(He),irradiation-induced microstructural changes,and the corresponding thermal-mechanical property degradation of W are critically important but are not well understood yet.Predicting the performance of W in fusion environments requires understanding the fundamentals of He-defect interactions and the resultant He bubble nucleation and growth in W.In this study,He retention in helium-ion-implanted W was assessed using neutron depth profiling(NDP),laser ablation mass spectrometry(LAMS),and thermal desorption spectroscopy(TDS)following 10 keV room-temperature He implantation at various fluences.These three experimental techniques enabled the determination of the He depth profile and retention in He-implanted W.A cluster dynamics model based on the diffusion-reaction rate theory was applied to interpret the experimental data.The model successfully predicted the He spatial depth-dependent profile in He-implanted W,which was in good agreement with the LAMS measurements.The model also successfully captured the major features of the He desorption spectra observed in the THDS measurements.The NDP quantified total He concentration values for the samples;they were similar to those estimated by LAMS.However,the depth profiles from NDP and LAMS were not comparable due to several factors.The combination of modeling and experimentation enabled the identification of possible trapping sites for He in W and the evolution of He-defect clusters during the TDS thermal annealing process.
基金This work was supported by the Special Fund for Agro-scientific Research in the Public Interest (Projected No.201303075)the Earmarked Fund for Modern Agro-industry Technology Research System (Projected No.CARS-26-22)。
文摘Nondestructive evaluation of melon quality is in great need of comprehensive study.Soluble solids content(SSC)and firmness are the two indicators of melon internal quality that mostly a®ect consumer acceptance.To provide guidance for fruit classification,internal quality standards was preliminarily established through sensory test,as:Melon with SSC over 12Brix,firmness 4–5.5 kgf·cm^(-2)2 were considered as satisfactory class sample;and SSC over 10Brix,¯rmness 3.5–6.5 kgf·cm^(-2) as average class sample.The near infrared(NIR)nondestructive detection program was set as spectra collected from the stylar-end,Brix expressed by the average SSC of inner and outer mesocarp,each cultivar of melon was detected with its own optimum integration time,and the second derivative algorithm was used to equalize them.Using wavelength selected by genetic algorithms(GA),a robust SSC model of mix-cultivar melon was established,the root mean standard error of cross-validation(RMSECV)was 0.99 and the ratio performance deviation(RPD)nearly reached 3.0,which almost could meet the accuracy requirement of 1.5Brix.Firmness model of mix-cultivar melon was acceptable but inferior.
基金National Natural Science Foundation of China(No.61201412)
文摘The relationship between ultrasonic nonlinearity and microstructure of the liner was studied during the whole curing process by ultrasonic transmission method and infrared spectroscopy.Nonlinearity of input instrumentation was minimized by the natural filtering effect of piezoelectric discs and the maximum excitation energy was acquired simultaneously so as to improve the accuracy of the measuring data.The experimental results indicate that in the liner curing reaction at40℃ultrasonic nonlinearity parameter decreases gradually after a sharp decline,which is consistent with the outcome of infrared spectroscopy as the curing degree increases.The research suggests an effective nondestructive approach to detect the curing properties of the liner in a nonlinear ultrasonic way.
基金Project(JCKY2016212A506-0503) supported by the Science Challenge Project of ChinaProject(51475106) supported by the National Natural Science Foundation of China
文摘Subsurface defects were fluorescently tagged with nanoscale quantum dots and scanned layer by layer using confocal fluorescence microscopy to obtain images at various depths. Subsurface damage depths of fused silica optics were characterized quantitatively by changes in the fluorescence intensity of feature points. The fluorescence intensity vs scan depth revealed that the maximum fluorescence intensity decreases sharply when the scan depth exceeds a critical value. The subsurface damage depth could be determined by the actual embedded depth of the quantum dots. Taper polishing and magnetorheological finishing were performed under the same conditions to verify the effectiveness of the nondestructive fluorescence method. The results indicated that the quantum dots effectively tagged subsurface defects of fused-silica optics, and that the nondestructive detection method could effectively evaluate subsurface damage depths.
基金Project supported by the National Natural Science Foundation of China (No. 30370371) and Program for New Century Excellent Talents in University (No. NCET-04-0524), China
文摘Watermelon is a popular fruit in the world with soluble solids content （SSC） being one of the major characteristics used for assessing its quality. This study was aimed at obtaining a method for nondestructive SSC detection of watermelons by means of visible/near infrared （Vis/NIR） diffuse transmittance technique. Vis/NIR transmittance spectra of intact watermelons were acquired using a low-cost commercially available spectrometer operating over the range 350-1000 nm. Spectra data were analyzed by two multivariate calibration techniques： partial least squares （PLS） and principal component regression （PCR） methods. Two experiments were designed for two varieties of watermelons [Qilin （QL）, Zaochunhongyu （ZC）], which have different skin thickness range and shape dimensions. The influences of different data preprocessing and spectra treatments were also investigated. Performance of different models was assessed in terms of root mean square errors of calibration （RMSEC）, root mean square errors of prediction （RMSEP） and correlation coefficient （r） between the predicted and measured parameter values. Results showed that spectra data preprocessing influenced the performance of the calibration models. The first derivative spectra showed the best results with high correlation coefficient of determination [r=0.918 （QL）; r=0.954 （ZC）], low RMSEP [0.65 °Brix （QL）; 0.58 °Brix （ZC）], low RMSEC [0.48 °Brix （QL）; 0.34°Brix （ZC）] and small difference between the＇RMSEP and the RMSEC by PLS method. The nondestructive Vis/NIR measurements provided good estimates of SSC index of watermelon, and the predicted values were highly correlated with destructively measured values for SSC. The models based on smoothing spectra （Savitzky-Golay filter smoothing method） did not enhance the performance of calibration models obviously. The results indicated the feasibility of Vis/NIR diffuse transmittance spectral analysis for predicting watermelon SSC in a nondestructive way.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61705061 and 61975053)Key Science and Technology Program of Henan Province of China(Grant Nos.182102110204 and 192102110047)+1 种基金Key Scientific and Research Project of Educational Committee of Henan Province of China(Grant No.19B510001)Open Fund Project of Key Laboratory of Grain Information Processing&Control,Ministry of Education,Henan University of Technology(Grant Nos.KFJJ2016108 and KFJJ2017107).
文摘t The aim of this study was to investigate the feasibility of detecting potassium sorbate(PS)and sorbic acid(SA)in agricultural products using THz time-domain spectroscopy(THz-TDS).The absorption spectra of PS and SA were measured from 0.2 to 1.6 THz at room temperature.The main characteristic absorption peaks of PS and SA in polyethylene and powdered agricultural products with different weight ratios were detected and analyzed.Interval partial least squares(iPLS)combined with a particle swarm optimization and support vector classification(PSO-SVC)algorithm was proposed in this paper.iPLS was used for frequency optimization,and the PSO-SVC algorithm was used for spectrum analysis of the preservative content based on the optimal spectrum ranges.Optimized PSO-SVC models were obtained when the THz spectrum from the PS/SA mixture was divided into 11 or 12 subintervals.The optimal penalty parameter c and kernel parameter g were found to be 1.284 and 0.863 for PS(0.551-1.487 THz),1.374 and 0.906 for SA(0.454-1.216 THz),respectively.The preliminary results indicate that THz-TDS can be an effective nondestructive analytical tool used for the quantitative detection of additives in agricultural products.
基金The project was financially supported by the National Natural Science Foundation of China （No. 50479027）.
文摘This paper presents a new algorithm to predict locations and severities of damage in structures by changing modal parameters. An existing algorithm of damage detection is reviewed and the new algorithm is formulated to improve the accuracy of damage locating and severity estimation by eliminating the erratic assumptions and limits in the existing algorithm. The damage prediction accuracy is numerically assessed for each algorithm when applied to a two-dimensional frame structure for which pre-damage and post-damage modal parameters are available for only a few modes of vibration. The analysis results illustrate the improved accuracy of the new algorithm when compared to the existing algorithm.
基金Supported by the National Natural Science Foundation of China(50674046)National Natural Science Important Foundation of China(50634050)National Basic Research Program of China(2007CB209400)
文摘According to the structure and stress trait of bearing bolts,a lateral-vibrationmechanics model was established for them,and the relation between lateral-vibration frequencyand axial load was analyzed;then,lateral-vibration trait of bearing bolts was studiedthrough laboratory simulation test.The results indicate that vibration frequency of boltsupport system increases as well as axial force,the detection on axial load of bolts can bemade by generating lateral vibration of bearing bolts.Theoretical and experimental researchresults show that frequency method is effective for detecting the axial force of boltsupport system.
基金financially supported by Shandong Provincial Natural Science Foundation,China(Grant No.ZR2022MC067)the National Key R&D Program of China(Grant No.2021YFB3901303)+2 种基金the Key R&D Program of Shandong Province,China(Grant No.2022 CXGC010610)the Agricultural Scientific and Technological Innovation Project of Shandong Academy of Agricultural Sciences,China(Grant No.CXGC2023D02)the Special International Cooperation Program of Shandong Academy of Agricultural Sciences,China(Grant No.CXGC2023G24).
文摘Recent approaches to the internal quality inspection of apples with the application of hyperspectral imaging technology are highly cost-intensive because of labor involvement for the data collection on a fixed posture and manual selection of the region of interest(RoI).In addition,several studies have repeated the data acquisition for the same apple.Current methods cannot meet the automation requirements of the sorting line.Therefore,this study proposed a novel method for automatically selecting RoI in hyperspectral images of apples with random poses.Firstly,the preliminary RoI selection of apple hyperspectral image was carried out,followed by the performance of histogram statistics of each pixel with spectral intensity at 700 nm wavelength.The top 40%area of the spectral intensity was reserved to obtain the magnitude relationship of the spectral intensity of each pixel point and a morphological erosion operation.Original apple RoI was acquired and overexposed pixels were removed with spectral intensity greater than 3900(maximum 4095)in the reserved area at 700 nm.Secondly,the relationship between apple size and prediction accuracy was measured for the in-depth RoI analysis.A partial least square regression(PLSR)model was established between the average spectrum and apple sugar content of RoI with different sizes.Finally,the established model with the top 70%of the spectral intensity achieved the best prediction accuracy.Non-destructive estimation of apple sugar content was performed through hyperspectral imaging technology with reference to the proposed RoI selection method.A competitive adaptive reweighted sampling algorithm along the PLSR(CARS-PLSR)model was established after black-and-white correction and standard normal transformation(SNV)preprocessing and obtained the highest prediction accuracy.The determination coefficient of cross-validation(R_(cv))and root mean square error of cross-validation(RMSECV)were 0.9595 and 0.3203°Brix,respectively.The determination coefficient of prediction(R_(p))was 0.9308,and the root mean square error of prediction(RMSEP)was 0.4681°Brix.Results proved that the auto-selection of RoI is an efficient and accurate method,which can provide a foundation in practical application for online apple grading systems with hyperspectral imaging technology.
基金supported by the National Key R&D Program of China(No.2022YFB2903800)the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2023C01139)the National Natural Science Foundation of China(No.62471433)。
文摘In this paper,we propose a photonic terahertz(THz)continuous-wave computed tomography(CT)system employing an optical frequency comb and specialized imaging algorithms.Our work leverages the system to offer unique advantages in detecting and analyzing samples that are challenging for traditional 2D scanning systems.Our experimental results,operating at 330 GHz,reach an exceptionally low amplitude standard deviation of 0.016 m V.Additionally,the proposed system performs nondestructive CT detection with a 0.5 mm error margin and obtains enhanced image quality,showing its great promise for implementing THz-CT imaging with high robustness and resolution.
基金The project was supported by National Natural Science Foundation of China(Grant No.31701318 and 31601216)National Natural Science Foundation of China Projects of International Cooperation and Exchanges(Grant No.31811540396)+1 种基金National Key Research and Development Program of China(Grant No.2017YFD0701205)Doctoral Research Fund of Liaoning Province,China(Grant No.20170520202).
文摘A new method to predict the seed vigor of rice was developed to control adulteration during the seed trading process and to address the deficiencies of traditional manual detection methods.Low-field nuclear magnetic resonance(LF-NMR)technique was used to detect the vigor of rice seeds.Four varieties(Beijing-1,Qianchonglang-2,Yanfeng-47 and Shennong-265)of rice seeds from the Rice Research Institute of Shenyang Agricultural University were chosen for the experiment.The transverse relaxation time T_(2),T_(21) and T_(22) were observed in the experiment.The peak start time of free water(transverse relaxation time T_(22)),signal amplitude of bound water(transverse relaxation time T_(21)),and moisture content decreased with the decrease in the vigor of the seeds.There were no obvious trends observed for the top of the peak and the end point of the transverse relaxation time T_(22).In addition,the start,top,and end time of the peak(transverse relaxation time T_(21)),and the signal amplitude of bound water showed no consistent changes.The results indicated that LF-NMR could be used as a method to distinguish the vigor of rice seeds rapidly.This study provided theoretical basis and technical support for the rapid detection of rice seed vigor.
基金supported by the Zhejiang Province Key Research and Development Program(Grant No.2021C02011)Zhejiang Province Public Welfare Technology Application Research Project(Grant No.LGN18-F030002)+3 种基金Hangzhou Science and Technology Bureau(Grant No.20201203B116)Program of“Xinmiao”(Potential)Talents in Zhejiang Province(Grant Number:2022R4-07B055)the Graduate Scientific Research Foundation of Hangzhou Dianzi University(Grant No.CXJJ2022177)the Major Science and Technology Projects of Breeding New Varieties of Agriculture in Zhejiang Province(Grant No.2021C02074).
文摘Nowadays,China stands as the global leader in terms of potato planting area and total potato production.The rapid and nondestructive detection of the potato quality before processing is of great significance in promoting rural revitalization and augmenting farmers’income.However,existing potato quality sorting methods are primarily confined to theoretical research,and the market lacks an integrated intelligent detection system.Therefore,there is an urgent need for a post-harvest potato detection method adapted to the actual production needs.The study proposes a potato quality sorting method based on cross-modal technology.First,an industrial camera obtains image information for external quality detection.A model using the YOLOv5s algorithm to detect external green-skinned,germinated,rot and mechanical damage defects.VIS/NIR spectroscopy is used to obtain spectral information for internal quality detection.A convolutional neural network(CNN)algorithm is used to detect internal blackheart disease defects.The mean average precision(mAP)of the external detection model is 0.892 when intersection of union(IoU)=0.5.The accuracy of the internal detection model is 98.2%.The real-time dynamic defect detection rate for the final online detection system is 91.3%,and the average detection time is 350 ms per potato.In contrast to samples collected in an ideal laboratory setting for analysis,the dynamic detection results of this study are more applicable based on a real-time online working environment.It also provides a valuable reference for the subsequent online quality testing of similar agricultural products.
